Unfortunately it is not easy to identify those at risk of suicide. A large number of variables and complex interactions mean only a trained clinician can assess for immediate risk of suicide.

However, the majority of people who take their own lives attend services other than mental health in their final year for apparently unrelated reasons. Now new research has looked at ways of using this health information to identify the most vulnerable.

The research was carried out by a team including Dr Marcos del Pozo Banos, Professor Ann John, Professor Damon Berridge and Professor Keith Lloyd from Swansea University’s Medical School, and Dr Caroline Jones of the University’s Hillary Rodham Clinton School of Law, in collaboration with academic colleagues from European universities as well as Dr Sarah Spencer, emergency medicine consultant at Princess of Wales Hospital in Bridgend.

Their findings, which have just been published, revealed that more than 80 per cent of suicide cases studied had had at least one contact with their GP during their last year.

Dr del Pozo Banos said: “We wanted to see if we could develop an algorithm that analyses routinely collected health data to flag people, so that when patients present with seemingly unrelated conditions, practitioners can ask them appropriate questions about their thoughts and feelings if required. “

He explained the crucial role artificial intelligence (AI) can now play in processing millions of records with thousands of variables to build a risk model very quickly. It can then process the health history of patients and highlight those who may be at risk.

Dr del Pozo Banos, an expert in mental health informatics and AI, said that by using primary care data it was possible to substantially increase the coverage of the system compared to other published proposals.

The research was co-authored by Professor Nicolai Petkov, from the University of Groningen, Netherlands; Kate Southern, from Cardiff Adult Self Injury Project; and Professor Carlos Manuel Travieso, of the University of Las Palmas de Gran Canaria, Spain.

It used the University’s SAIL Databank to obtain information about more than 2,000 suicides held in SID-Cymru - an electronic cohort of all deaths by suicide and of undetermined intent in Wales since 2001 - plus 20 controls per case.

Dr del Pozo Banos described their preliminary results, which recorded almost 75 per cent accuracy, as very promising.

The team now plans to gradually increase the complexity of the AI system to improve the tool’s precision before carrying out thorough trials.

He said: “Our proposal will not replace clinical assessment of immediate risk, but we hope it will help clinicians to identify vulnerable people when they access health services so that the right questions may be asked.

“We are a long way from employing such methods in clinical practice, but this is a first step to harnessing the potential of routinely collected electronic health records to support clinical practice in real time.”

Professor John added: “AI methods have huge potential to improve suicide prevention, and this is an exciting development.

“However, we are still at the early development stages before any practical use. Collaborations between clinicians, academics (both from a traditional epidemiological background and data scientists), ethical experts, patients and people who use servicesare vital for the development of these innovative tools."